GS 3: Awareness in the fields of IT, space, computers, robotics, computers, biotechnology and issues related to intellectual property rights.
GS 3: Infrastructure: Energy, ports, roads, airports, railways, etc
Data is an essential piece for unlocking maximum value within a transportation system. Data analytics, and data sharing between organizations, has the potential to create more efficient passenger mobility, as well as allow for optimal designing of transit routes and services, infrastructure, and regulations. Data collection is also essential to enabling the use of a number of emerging technologies in the mobility space, such as blockchain and artificial intelligence. Using data to optimize commutes and transit infrastructure will lead to lower levels of congestion, reduced tailpipe emissions, and less time spent in transit, resulting in cities that are cleaner, safer, better designed, and more economically prosperous.
What is Mobility data?
Data can be defined as “representation of information, facts, concepts, opinions, or instructions in a manner suitable for communication, interpretation, or processing by humans or by automated means.” Mobility data can include a wide range of data related to transport. This includes data of public transit agencies, private mobility solution providers, and individual citizens. In addition to information about transportation assets and trips, mobility data can also include data that affects or goes in tandem with mobility, such as weather data (can affect traffic, etc.), pollution and air quality data, and traffic violations data.
Who owns Mobility data?
At the highest level, the three categories of stakeholders most critical to the data landscape are data owners, beneficiaries, and government. Data owners are companies, organizations, and individuals that produce and own datasets. Beneficiaries are groups and individuals that can benefit from using the data owned by the data owners.
In addition to both benefiting from and owning data, government can play a key role in enabling the interactions between these two stakeholder groups and protecting their interests. It should be noted that these categories are not rigid: data can flow from owners to beneficiaries, but it can also flow within each of these categories, and some beneficiaries may also qualify as data owners. There are many cases of business to business data sharing; for example, Uber uses data from Google Maps to provide its own service.
Public and private data
Within the category of data ownership, there is one primary distinction: public versus private data ownership. Public data is shared openly, irrespective of how it is generated. Private data is kept within an organization and not shared with the public. Data owners may have both private and public datasets. Data generated by private service providers is important in India because of a significant presence of privately-owned or operated transport services that meet the passenger mobility needs in cities.
Mobility as a service
Mobility as a Service, or MaaS, refers to the technology-enabled, on-demand availability of multimodal trip options, including multimodal trip planning and seamless payment. Mobility as a service is an alternative to private vehicle ownership; travellers should be able to order a ride to wherever they need to go, at the time they need and in whatever size or type of vehicle meets their needs.
Under the MaaS paradigm, the entire transportation system functions as a cooperative, interconnected system to meet travellers’ needs through a variety of transport modes. To achieve this, infrastructure, technology platforms, payment, transportation services, and data analysis must be capable of working together.
A number of emerging technologies have potential applications in the mobility sector:
Big data analytics: Data analytics refers to the practice of examining large amounts of data to discover patterns, correlations, and other insights. Data analytics and technologies can allow businesses, governments, and other organizations to analyse datasets and draw conclusions to help them make informed decisions. Big data analytics includes a wide range of specific tools and techniques that can be used to gather insights from data; some specific techniques include data mining, predictive analytics, and text mining. For mobility, big data analytics can help with planning and managing transportation networks and designing and optimizing services to meet transportation needs. Both the private and public sector can benefit from big data analytics for mobility.
Blockchain: Blockchain stands ready to revolutionize significant portions of the transportation space and could be particularly applicable in the Indian context. Blockchain allows for more decentralized transactions through a shared, standardized ledger system, which could allow decentralized passenger and freight systems in India to improve their efficiency without the oversight of a central governing body.
For vehicle sharing, customers often have a set of preferences and desires they wish in both their vehicle and their fellow car sharing customers. Blockchain enables a customer to have a secure area for these preferences to be logged, as well as previous experiences in car sharing. Through blockchain encryption, this is a permanent, immutable ledger.
Advanced Driver Assistance Systems (ADAS): Artificial intelligence can help to automate, adapt, and enhance vehicle systems for safety and better driving, to reduce the potential for human error. These technologies may increase safety and reduce collisions and accidents by alerting the driver to potential problems, implementing safeguards, and taking over control of the vehicle to varying degrees.
Challenges to data acquisition and usage
Privacy and data security: As big data becomes more prevalent and necessary in developing effective mobility systems, concerns have been raised about protecting individuals’ privacy. Personally identifiable information (PII) is generally considered information that can be used on its own or with other information to identify, contact, or locate an individual person. Individuals are generally concerned about leaks of PII because they can result in negative consequences.
Poor quality and incomplete data: Data collection in India is insufficient and in some cases the data that is collected is of poor quality or incomplete. A dataset may be considered of poor quality if it fails to provide adequate or accurate information. For example, real-time location data is often inaccurate, collected infrequently, or restricted to only certain services; a transit agency may be inconsistent about stop and route identifiers, or a bus equipped with GPS may have a system that is broken or inaccurate.
Acquiring data from private data owners: Acquiring data from private data owners is often one of the biggest challenges in collecting the data needed for a particular use case. These data owners tend to be concerned primarily with jeopardizing their competitive advantage by sharing their private data.
Data has an important role to play in helping India achieve a mobility system that is clean, efficient, and adequately supports the mobility needs of its citizens. To ensure the maximum benefit of mobility data, steps should be taken to collect and share data and ensure that the data collected by different parties is made available as much as possible. Effective communication between data owners and potential beneficiaries is at the core of reaching this outcome; to effectively communicate and collaborate, all stakeholders involved must understand the landscape of the system and the motivations and risks of the various parties involved.
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